RR 319 Machine Learning with Tyler Renelle

RR 319 Machine Learning with Tyler Renelle

This episode of the Ruby Rogues Panel features panelists Charles Max Wood and Dave Kimura. Tyler Renelle, who stops by to talk about machine learning, joins them as a guest. Tyler is the first guest to talk on Adventures in Angular, JavaScript Jabber, and Ruby Rogues. Tune in to find out more about Tyler and machine learning!

What is machine learning?

Machine learning is a different concept than programmers are used to.

There are three phases in computing technology.

First phase – building computers in the first place but it was hard coded onto the physical computing machinery

Second phase – programmable computers. Where you can reprogram your computer to do anything. This is the phase where programmers fall.

Third phase – machine learning falls under this phase.

Machine learning is where the computer programs itself to do something. You give the computer a measurement of how it’s doing based on data and it trains itself and learns how to do the task. It is beginning to get a lot of press and become more popular. This is because it is becoming a lot more capable by way of deep learning.

AI – Artificial Intelligence

Machine learning is a sub field of artificial intelligence. AI is an overarching field of the computer simulating intelligence. Machine learning has become less and less a sub field over time and more a majority of AI. Now we can apply machine learning to vision, speech processing, planning, knowledge representation. This is fast taking over AI. People are beginning to consider the terms artificial intelligence and machine learning synonymous.

Self-driving cars are a type of artificial intelligence. The connection between machine learning and self-driving cars is abstract. A fundamental thing in self-driving cars is machine learning. You program the car as to how to fix its mistakes. Another example is facial recognition. The program starts learning about a person’s face over time so it can make an educated guess as to if the person is who they say they are. Once statistics are added then your face can be off by a hair or a hat. Small variations won’t throw it off.

How do we start solving the problems we want to be solved?

Machine learning has been applied since the 1950s to a broad spectrum of problems. Have to have a little bit of domain knowledge and do some research.

Machine Learning Vs Programming

Machine learning is any sort of fuzzy programming situation. Programming is when you do things specifically or statically.

Why should you care to do machine learning?

People should care because this is the next wave of computing. There is a theory that this will displace jobs. Self-driving cars will displace truck drivers, Uber drivers, and taxis. There are things like logo generators already. Machines are generating music, poetry, and website designs. We shouldn’t be afraid that we should keep an eye towards it.

If a robot or computer program or AI were able to write its own code, at what point would it be able to overwrite or basically nullify the three laws of robotics?

Nick Bostrom wrote the book Superintelligence, which had many big names in technology talking about the dangers of AI. Artificial intelligence has been talked about widely because of the possibility of evil killer robots in the Sci-Fi community. There are people who hold very potential concerns, such as job automation.

Consciousness is a huge topic of debate right now on this topic. Is it an emergent property of the human brain? Is what we have with deep learning enough of a representation to achieve consciousness? It is suggested that AI may or may not achieve consciousness. The question is if it is able to achieve consciousness – will we be able to tell there isn’t a person there?

The main language used for machine learning is Python. This is not because of the language itself, but because of the tools built on top of it. The main framework is TensorFlow. Python in TensorFlow drops to C and executes code on the GPU for performing matrix algebra, which is essential for deep learning. You can always use C, C++, Java, and R. Data scientists mostly use R, while researchers use C and C++ so they can custom code their matrix algebra themselves.